Ensemble Learning for Fetal Health Classification

Duhayyim, Mesfer Al; Abbas, Sidra; Hejaili, Abdullah Al; Kryvinska, Natalia; Almadhor, Ahmad; Mughal, Huma

doi:10.32604/csse.2023.037488

Cited by 2 publications

(2 citation statements)

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“…Nonetheless, the prevalent reliance on a single dataset in studies [25][26][27][28] highlights a significant research gap, underscoring the imperative of employing more diverse datasets to substantiate these models' validity. In the context of ensemble techniques, especially Gradient Boosting (GB) models, studies [41,[43][44][45] have yielded outstanding results. Yet, the repeated use of similar datasets, with study [44] being an exception as it utilized a unique private dataset, raises concerns about the diversity of datasets.…”

Section: Discussionmentioning

confidence: 99%

“…Next, in a study by Al Duhayyim et al [45], the paper aimed to identify the abnormal, suspicious, and pathological fetus readings in the CTG results that are imbalanced. Moreover, automating the process of classifying fetal health is necessary in order to obtain a prompt and precise diagnosis of both fetal and maternal health.…”

Section: Fetal Hypoxia During Labor Using Ensemblementioning

confidence: 99%

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Fetal Hypoxia Detection Using Machine Learning: A Narrative Review

Alharbi,

Youldash,

Alotaibi

et al. 2024

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Fetal hypoxia is a condition characterized by a lack of oxygen supply in a developing fetus in the womb. It can cause potential risks, leading to abnormalities, birth defects, and even mortality. Cardiotocograph (CTG) monitoring is among the techniques that can detect any signs of fetal distress, including hypoxia. Due to the critical importance of interpreting the results of this test, it is essential to accompany these tests with the evolving available technology to classify cases of hypoxia into three cases: normal, suspicious, or pathological. Furthermore, Machine Learning (ML) is a blossoming technique constantly developing and aiding in medical studies, particularly fetal health prediction. Notwithstanding the past endeavors of health providers to detect hypoxia in fetuses, implementing ML and Deep Learning (DL) techniques ensures more timely and precise detection of fetal hypoxia by efficiently and accurately processing complex patterns in large datasets. Correspondingly, this review paper aims to explore the application of artificial intelligence models using cardiotocographic test data. The anticipated outcome of this review is to introduce guidance for future studies to enhance accuracy in detecting cases categorized within the suspicious class, an aspect that has encountered challenges in previous studies that holds significant implications for obstetricians in effectively monitoring fetal health and making informed decisions.

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